System performance logging of complex remote query processor query operations

• US 9,886,469 B2
• Filed: 05/14/2016
• Issued: 02/06/2018
• Est. Priority Date: 05/14/2015
• Status: Active Grant

• Current Assignee: Deephaven Data Labs, LLC
• Original Assignee: Walleye Software LLC
• Inventors: Kent, IV, David R., Caudy, Ryan, Wright, Charles, Teodorescu, Radu
• Primary Examiner(s): Mofiz, Apu
• Assistant Examiner(s): Glasser, Dara J
• Application Number:
  US15/154,980
• Publication Number:
  US 20160335306A1
• Time in Patent Office: 633 Days
• CPC Class Codes: G06F 11/1451 by selection of backup contents
• CPC Class Codes: G06F 11/1464 for networked environments
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• CPC Class Codes: G06F 12/0261 using reference counting
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• CPC Class Codes: G06F 16/113 Details of archiving lifecycle management in storage systems G06F3/0649; backup systems G06F11/1446
• CPC Class Codes: G06F 16/144 Query formulation
• CPC Class Codes: G06F 16/162 Delete operations erasing in storage systems G06F3/0652
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
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• CPC Class Codes: G06F 16/2264 Multidimensional index structures
• CPC Class Codes: G06F 16/2272 Management thereof
• CPC Class Codes: G06F 16/2282 Tablespace storage structures; Management thereof
• CPC Class Codes: G06F 16/2291 : User-Defined Types; Storage management thereof
• CPC Class Codes: G06F 16/23 : Updating
• CPC Class Codes: G06F 16/2308 : Concurrency control transaction processing G06F9/466
• CPC Class Codes: G06F 16/2358 : Change logging, detection, and notification replication G06F16/27
• CPC Class Codes: G06F 16/2365 : Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/2372 : Updates performed during offline database operations
• CPC Class Codes: G06F 16/2379 : Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/242 : Query formulation
• CPC Class Codes: G06F 16/2428 : Query predicate definition using graphical user interfaces, including menus and forms G06F16/2423 takes precedence
• CPC Class Codes: G06F 16/245 : Query processing
• CPC Class Codes: G06F 16/2453 : Query optimisation
• CPC Class Codes: G06F 16/24534 : Query rewriting; Transformation
• CPC Class Codes: G06F 16/24535 : of sub-queries or views
• CPC Class Codes: G06F 16/24537 : of operators
• CPC Class Codes: G06F 16/24539 : using cached or materialised query results
• CPC Class Codes: G06F 16/2455 : Query execution
• CPC Class Codes: G06F 16/24553 : of query operations
• CPC Class Codes: G06F 16/2456 : Join operations
• CPC Class Codes: G06F 16/24561 : Intermediate data storage techniques for performance improvement
• CPC Class Codes: G06F 16/2457 : with adaptation to user needs
• CPC Class Codes: G06F 16/24575 : using context
• CPC Class Codes: G06F 16/248 : Presentation of query results
• CPC Class Codes: G06F 16/254 : Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/27 : Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
• CPC Class Codes: G06F 16/278 : Data partitioning, e.g. horizontal or vertical partitioning
• CPC Class Codes: G06F 16/285 : Clustering or classification
• CPC Class Codes: G06F 16/9024 : Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/907 : Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/951 : Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 : Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 : Presentation of query results
• CPC Class Codes: G06F 16/9566 : URL specific, e.g. using aliases, detecting broken or misspelled links
• CPC Class Codes: G06F 16/9574 : of access to content, e.g. by caching
• CPC Class Codes: G06F 17/40 : Data acquisition and logging for input to computer G06F3/00
• CPC Class Codes: G06F 21/00 : Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• CPC Class Codes: G06F 21/6209 : to a single file or object, e.g. in a secure envelope, encrypted and accessed using a key, or with access control rules appended to the object itself
• CPC Class Codes: G06F 2201/805 : Real-time
• CPC Class Codes: G06F 2201/84 : Using snapshots, i.e. a logical point-in-time copy of the data
• CPC Class Codes: G06F 2212/1052 : Security improvement
• CPC Class Codes: G06F 2212/154 : Networked environment
• CPC Class Codes: G06F 2212/163 : Server or database system
• CPC Class Codes: G06F 2212/60 : Details of cache memory
• CPC Class Codes: G06F 3/0481 : based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/0482 : Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/0483 : Interaction with page-structured environments, e.g. book metaphor
• CPC Class Codes: G06F 3/04847 : Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06F 3/0485 : Scrolling or panning
• CPC Class Codes: G06F 3/04895 : Guidance during keyboard input operation, e.g. prompting
• CPC Class Codes: G06F 3/0605 : by facilitating the interaction with a user or administrator
• CPC Class Codes: G06F 3/0656 : Data buffering arrangements
• CPC Class Codes: G06F 3/067 : Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
• CPC Class Codes: G06F 40/117 : Tagging; Marking up details of markup languages G06F40/143; Designating a block; Setting of attributes style sheets, e.g. eXtensible Stylesheet Language Transformation [XSLT], G06F40/154
• CPC Class Codes: G06F 40/134 : Hyperlinking
• CPC Class Codes: G06F 40/166 : Editing, e.g. inserting or deleting
• CPC Class Codes: G06F 40/174 : Form filling; Merging
• CPC Class Codes: G06F 40/177 : of tables; using ruled lines
• CPC Class Codes: G06F 40/18 : of spreadsheets form-filling G06F40/174
• CPC Class Codes: G06F 40/183 : Tabulation, i.e. one-dimensional positioning
• CPC Class Codes: G06F 40/216 : using statistical methods
• CPC Class Codes: G06F 40/274 : Converting codes to words; Guess-ahead of partial word inputs
• CPC Class Codes: G06F 8/30 : Creation or generation of source code
• CPC Class Codes: G06F 8/41 : Compilation
• CPC Class Codes: G06F 8/427 : Parsing
• CPC Class Codes: G06F 8/60 : Software deployment
• CPC Class Codes: G06Q 40/04 : Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: H04L 12/18 : for broadcast or conference , e.g. multicast
• CPC Class Codes: H04L 51/046 : Interoperability with other network applications or services
• CPC Class Codes: H04L 51/212 : using filtering or selective blocking
• CPC Class Codes: H04L 61/5069 : for group communication, multicast communication or broadcast communication
• CPC Class Codes: H04L 63/101 : Access control lists [ACL]
• CPC Class Codes: H04L 63/102 : Entity profiles
• CPC Class Codes: H04L 67/01 : Protocols
• CPC Class Codes: H04L 67/1001 : for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/141 : Setup of application sessions admission control or resource allocation in data switching networks H04L47/70
• CPC Class Codes: H04L 67/34 : involving the movement of software or configuration parameters network booting or remote initial program loading [RIPL] G06F9/4416
• CPC Class Codes: H04L 67/56 : Provisioning of proxy services store-and-forward switching systems in data switching networks H04L12/54
• CPC Class Codes: H04L 67/566 : Grouping or aggregating service requests, e.g. for unified processing
• CPC Class Codes: H04L 67/568 : Storing data temporarily at an intermediate stage, e.g. caching
• CPC Class Codes: H04L 67/5681 : Pre-fetching or pre-delivering data based on network characteristics
• CPC Class Codes: H04L 69/16 : Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]

Abstract

Described are methods, systems and computer readable media for performance logging of complex query operations.

Claims

1. A system for improving performance of a remote query computer system by using dynamic query performance logging to identify and remediate efficiency impediments in the remote query computer system, the system comprising:

one or more processors;

computer readable storage coupled to the one or more processors, the computer readable storage having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including;

receiving at a query server computer a digital request for a remote query processor from a client computer;

at the query server computer performing query server operations including;

receiving a digital request from the client computer to execute a query task;

executing the query task;

generating a set of query task data;

generating a set of query subtask data;

collecting the set of query task data;

digitally transmitting the set of query task data to one or more performance table loggers;

collecting the set of query subtask data;

transmitting the set of query subtask data to one or more performance table logger processes;

the one or more performance table logger processes writing the set of query task data to a first set of electronic data systems for subsequent retrieval and analysis;

the one or more performance table logger processes writing the set of query subtask data to a second set of electronic data systems for subsequent retrieval and analysis;

executing a query update, the query task comprising a recurring query operation, the executing comprising updating one or more dynamic components of the recurring query operation;

generating a set of query update data, the generating including;

listening for an update to one or more dynamic nodes of an update propagation graph, the update propagation graph being a structure implemented with connected nodes through which updates are propagated, each of the connected nodes being a static node or a dynamic node,

determining an occurrence of an update to one or more nodes of the update propagation graph, the determining the occurrence of an update to one or more nodes of the update propagation graph being based on a determination of one or more events without requiring a comparison of data results, and

creating data for each occurrence of an update to one or more nodes of the update propagation graph;

collecting the set of query update data;

writing the set of query update data to a third set of data systems for subsequent retrieval and analysis;

with a processor retrieving the set of query task data and the set of query subtask data;

using a performance-enhancing processor to analyze the retrieved set of query task data and the retrieved set of query subtask data to obtain an analysis result;

with the performance-enhancing processor, identifying based on the analysis result an efficiency impediment in the query server computer; and

responsive to the identifying, using a processor to make changes to stored instructions and thereby alleviate the identified efficiency impediment and improve efficiency of the remote query computer system,

wherein the efficiency impediment is one or more of;

a dataflow bottleneck, excessive use of processor resources, or excessive use of RAM, and

wherein the set of query task data includes an initial data from one or more nodes of the update propagation graph.

2. The system of claim 1, wherein the query server operations further include:

configuring a query update interval;

wherein the executing the query update includes executing the query update within the query update interval.

3. The system of claim 1, wherein the query server operations further include:

configuring a query update interval;

determining that the query update may not complete within the query update interval; and

extending the query update interval to allow a full execution of the query update.

4. The system of claim 1, wherein the query task includes at least one member of the group consisting of:

an instance of a class implementing a remote query interface;

a remote method invocation on a table handle object; and

a script or line of scripting code to be evaluated by an interpreter on a remote query processor.

5. The system of claim 1,

wherein the first set of data systems for subsequent retrieval and analysis and the second set of data systems for subsequent retrieval and analysis include at least one member of the group consisting of database tables and objects;

and

wherein the query task data includes performance data.

15. The system of claim 1, wherein the request for a remote query processor is a request to start a remote query processor on the query server.

16. The system of claim 1, wherein the query task comprises one or more subtasks, the set of query subtask data being generated from within the query task when the one or more subtasks are performed.

6. A method comprising:

receiving a digital request from a client computer to execute a query task;

executing the query task;

generating a set of query task data;

generating a set of query subtask data;

collecting the set of query task data;

digitally transmitting the set of query task data to one or more performance table loggers;

collecting the set of query subtask data;

transmitting the set of query subtask data to one or more performance table logger processes;

the one or more performance table logger processes writing the set of query task data to a first set of electronic data systems for subsequent retrieval and analysis;

the one or more performance table logger processes writing the set of query subtask data to a second set of electronic data systems for subsequent retrieval and analysis;

executing a query update, the query task comprising a recurring query operation, the executing comprising updating one or more dynamic components of the recurring query operation;

generating a set of query update data, the generating including;

listening for an update to one or more dynamic nodes of an update propagation graph, the update propagation graph being a structure implemented with connected nodes through which updates are propagated, each of the connected nodes being a static node or a dynamic node,

determining an occurrence of an update to one or more nodes of the update propagation graph, the determining the occurrence of an update to one or more nodes of the update propagation graph being based on a determination of one or more events without requiring a comparison of data results, and

creating data for each occurrence of an update to one or more nodes of the update propagation graph;

collecting the set of query update data;

writing the set of query update data to a third set of data systems for subsequent retrieval and analysis;

retrieving the set of query task data and the set of query subtask data;

analyzing the retrieved set of query task data and the retrieved set of query subtask data to obtain an analysis result;

identifying based on the analysis result an efficiency impediment in a query server computer system; and

making changes to stored instructions and thereby alleviating the identified efficiency impediment and improve efficiency of a remote query computer system,

wherein the efficiency impediment is one or more of;

a dataflow bottleneck, excessive use of processor resources, or excessive use of RAM, and

wherein the set of query task data includes an initial data from one or more nodes of the update propagation graph.

7. The method of claim 6, further comprising:

configuring a query update interval;

wherein the executing the query update includes executing the query update within the query update interval.

8. The method of claim 6, further comprising:

configuring a query update interval;

determining that the query update may not complete within the query update interval; and

extending the query update interval to allow a full execution of the query update.

9. The method of claim 6, wherein the query task includes at least one member of the group consisting of:

an instance of a class implementing a remote query interface;

a remote method invocation on a table handle object; and

a script or line of scripting code to be evaluated by an interpreter on a remote query processor.

10. The method of claim 6,

wherein the first set of data systems for subsequent retrieval and analysis and the second set of data systems for subsequent retrieval and analysis include at least one member of the group consisting of database tables and objects;

and

wherein the query task data includes performance data.

11. A nontransitory computer readable medium having stored thereon software instructions that, when executed by one or more processors, cause the one or more processors to perform operations including:

receiving a digital request from a client computer to execute a query task;

executing the query task;

generating a set of query task data;

generating a set of query subtask data;

collecting the set of query task data;

digitally transmitting the set of query task data to one or more performance table loggers;

collecting the set of query subtask data;

transmitting the set of query subtask data to one or more performance table logger processes;

the one or more performance table logger processes writing the set of query task data to a first set of electronic data systems for subsequent retrieval and analysis;

the one or more performance table logger processes writing the set of query subtask data to a second set of electronic data systems for subsequent retrieval and analysis;

executing a query update, the query task comprising a recurring query operation, the executing comprising updating one or more dynamic components of the recurring query operation;

generating a set of query update data, the generating including;

listening for an update to one or more dynamic nodes of an update propagation graph, the update propagation graph being a structure implemented with connected nodes through which updates are propagated, each of the connected nodes being a static node or a dynamic node,

determining an occurrence of an update to one or more nodes of the update propagation graph, the determining the occurrence of an update to one or more nodes of the update propagation graph being based on a determination of one or more events without requiring a comparison of data results, and

creating data for each occurrence of an update to one or more nodes of the update propagation graph;

collecting the set of query update data;

writing the set of query update data to a third set of data systems for subsequent retrieval and analysis;

retrieving the set of query task data and the set of query subtask data;

analyzing the retrieved set of query task data and the retrieved set of query subtask data to obtain an analysis result;

identifying based on the analysis result an efficiency impediment in a query server computer system; and

making changes to stored instructions and thereby alleviating the identified efficiency impediment and improve efficiency of a remote query computer system,

wherein the efficiency impediment is one or more of;

a dataflow bottleneck, excessive use of processor resources, or excessive use of RAM, and

wherein the set of query task data includes an initial data from one or more nodes of the update propagation graph.

12. The nontransitory computer readable medium of claim 11, wherein the operations further include:

configuring a query update interval;

wherein the executing the query update includes executing the query update within the query update interval.

13. The nontransitory computer readable medium of claim 11,

wherein the first set of data systems for subsequent retrieval and analysis and the second set of data systems for subsequent retrieval and analysis include at least one member of the group consisting of database tables and objects;

and

wherein the query task data includes performance data.

14. The nontransitory computer readable medium of claim 11, wherein the query task includes at least one member of the group consisting of:

an instance of a class implementing a remote query interface;

a remote method invocation on a table handle object; and

a script or line of scripting code to be evaluated by an interpreter on a remote query processor.

17. The nontransitory computer readable medium of claim 11, wherein the operations further include:

configuring a query update interval;

determining that the query update may not complete within the query update interval; and

extending the query update interval to allow a full execution of the query update.